Fourth Largest Rent A Car Brand: Business Program
This case study highlights the ability of Big Data Predictive Analytics to prevent adverse selection and micro-target the right customer prospect segment.
Client was a private equity funded start-up acquired from the number two largest rent a car organization in an FTC- mandated auction. When acquired, it had been used as a price leader and almost all volume came through the least profitable channel, online travel agencies like Expedia and Priceline. As a result, it had almost no repeat business; only 4% rented more than once.
Service initiatives were put in place that provided the amenities frequent renters [mostly business] required, such as expedited/express lines, automated billing and a better fleet. But in order to compete for the business traveler, even more was needed.
Client developed a business renter’s program that was unique to the Industry. It offered flat rates nationwide or the best rate available in every market with no blackout dates. The challenge was to acquire business accounts that would rent primarily Monday through Friday in major markets and to avoid the cost of acquiring businesses/renters who were loyal to one of the major brands. Furthermore, Client needed to avoid attracting price sensitive infrequent leisure travelers who might sign up but only use the firm once or twice to take advantage of the price and best customer benefits.
The firm utilized Big Data Predictive Analytics to build a behavioral profile of the desired business/renter and where they could be found online. This model was optimized over a three-month period testing everything from ad creative and landing pages to travel timeframe and time of day.
- Target: Selectively identify and acquire a very unique business traveler for a frequent traveler B2B program
- Target: Exclude typical price conscious leisure travelers. Adverse selection was important to prevent price shoppers from finding and using the program for one-off travel
- Target: Find Travelers who rent 6 + times a year, are unaffiliated with any competitor’s programs, who are value “biased” buyers, who rent mostly for business and are high value future prospects
- Using Big Data and predictive analytics we optimized display ads and followed these targeted prospects around the Web to their most frequently visited websites where they were served display ads
- The result: The firm acquired and activated 11K accounts in three months at an initial cost of sale of less than 10% [compared to an average of 20% through the OTA channel]
- Once acquired these accounts required no further investment which drove down cost of sale on each subsequent transaction